diff --git a/pl_examples/basic_examples/lightning_module_template.py b/pl_examples/basic_examples/lightning_module_template.py
index 07ca119651e06..50fba989ea768 100644
--- a/pl_examples/basic_examples/lightning_module_template.py
+++ b/pl_examples/basic_examples/lightning_module_template.py
@@ -1,7 +1,7 @@
 """
 Example template for defining a system
 """
-import logging
+import logging as log
 import os
 from argparse import ArgumentParser
 from collections import OrderedDict
@@ -215,17 +215,17 @@ def __dataloader(self, train):
 
     @pl.data_loader
     def train_dataloader(self):
-        logging.info('training data loader called')
+        log.info('Training data loader called.')
         return self.__dataloader(train=True)
 
     @pl.data_loader
     def val_dataloader(self):
-        logging.info('val data loader called')
+        log.info('Validation data loader called.')
         return self.__dataloader(train=False)
 
     @pl.data_loader
     def test_dataloader(self):
-        logging.info('test data loader called')
+        log.info('Test data loader called.')
         return self.__dataloader(train=False)
 
     @staticmethod
diff --git a/pytorch_lightning/__init__.py b/pytorch_lightning/__init__.py
index 49dd9591f0dd1..7c04927285868 100644
--- a/pytorch_lightning/__init__.py
+++ b/pytorch_lightning/__init__.py
@@ -25,14 +25,15 @@
     # We are not importing the rest of the scikit during the build
     # process, as it may not be compiled yet
 else:
+    import logging as log
+    log.basicConfig(level=log.INFO)
+
     from .trainer.trainer import Trainer
     from .core.lightning import LightningModule
     from .core.decorators import data_loader
-    import logging
 
     __all__ = [
         'Trainer',
         'LightningModule',
         'data_loader',
     ]
-    logging.basicConfig(level=logging.INFO)
diff --git a/pytorch_lightning/callbacks/pt_callbacks.py b/pytorch_lightning/callbacks/pt_callbacks.py
index 125e15e1e42c8..1c54f98a9fb9f 100644
--- a/pytorch_lightning/callbacks/pt_callbacks.py
+++ b/pytorch_lightning/callbacks/pt_callbacks.py
@@ -7,8 +7,9 @@
 
 import os
 import shutil
-import logging
+import logging as log
 import warnings
+
 import numpy as np
 
 from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel
@@ -113,7 +114,7 @@ def __init__(self, monitor='val_loss',
 
         if mode not in ['auto', 'min', 'max']:
             if self.verbose > 0:
-                logging.info(f'EarlyStopping mode {mode} is unknown, fallback to auto mode.')
+                log.info(f'EarlyStopping mode {mode} is unknown, fallback to auto mode.')
             mode = 'auto'
 
         if mode == 'min':
@@ -175,7 +176,7 @@ def on_epoch_end(self, epoch, logs=None):
 
     def on_train_end(self, logs=None):
         if self.stopped_epoch > 0 and self.verbose > 0:
-            logging.info(f'Epoch {self.stopped_epoch + 1:05d}: early stopping')
+            log.info(f'Epoch {self.stopped_epoch + 1:05d}: early stopping')
 
 
 class ModelCheckpoint(Callback):
@@ -351,7 +352,7 @@ def on_epoch_end(self, epoch, logs=None):
                         else:
                             self.best = max(self.best_k_models.values())
                         if self.verbose > 0:
-                            logging.info(
+                            log.info(
                                 f'\nEpoch {epoch:05d}: {self.monitor} reached'
                                 f' {current:0.5f} (best {self.best:0.5f}), saving model to'
                                 f' {filepath} as top {self.save_top_k}')
@@ -359,13 +360,13 @@ def on_epoch_end(self, epoch, logs=None):
 
                     else:
                         if self.verbose > 0:
-                            logging.info(
+                            log.info(
                                 f'\nEpoch {epoch:05d}: {self.monitor}'
                                 f' was not in top {self.save_top_k}')
 
             else:
                 if self.verbose > 0:
-                    logging.info(f'\nEpoch {epoch:05d}: saving model to {filepath}')
+                    log.info(f'\nEpoch {epoch:05d}: saving model to {filepath}')
                 self._save_model(filepath)
 
 
@@ -417,6 +418,6 @@ def on_epoch_begin(self, epoch, trainer):
 #     losses = [10, 9, 8, 8, 6, 4.3, 5, 4.4, 2.8, 2.5]
 #     for i, loss in enumerate(losses):
 #         should_stop = c.on_epoch_end(i, logs={'val_loss': loss})
-#         logging.info(loss)
+#         log.info(loss)
 #         if should_stop:
 #             break
diff --git a/pytorch_lightning/core/lightning.py b/pytorch_lightning/core/lightning.py
index 20008676543d0..0170b55e36afc 100644
--- a/pytorch_lightning/core/lightning.py
+++ b/pytorch_lightning/core/lightning.py
@@ -1,10 +1,10 @@
 import collections
-import logging
+import logging as log
+import csv
 import os
 import warnings
 from abc import ABC, abstractmethod
 from argparse import Namespace
-import csv
 
 import torch
 import torch.distributed as dist
@@ -1130,7 +1130,7 @@ def __init__(self, hparams):
 
     def summarize(self, mode):
         model_summary = ModelSummary(self, mode=mode)
-        logging.info('\n' + model_summary.__str__())
+        log.info('\n' + model_summary.__str__())
 
     def freeze(self):
         r"""
@@ -1213,7 +1213,7 @@ def on_save_checkpoint(self, checkpoint):
 
 def load_hparams_from_tags_csv(tags_csv):
     if not os.path.isfile(tags_csv):
-        logging.warning(f'Missing Tags: {tags_csv}.')
+        log.warning(f'Missing Tags: {tags_csv}.')
         return Namespace()
 
     tags = {}
diff --git a/pytorch_lightning/core/memory.py b/pytorch_lightning/core/memory.py
index 269282d5529c2..a1ae908d36782 100644
--- a/pytorch_lightning/core/memory.py
+++ b/pytorch_lightning/core/memory.py
@@ -3,7 +3,7 @@
 '''
 
 import gc
-import logging
+import logging as log
 import os
 import subprocess
 from subprocess import PIPE
@@ -214,7 +214,7 @@ def print_mem_stack():  # pragma: no cover
     for obj in gc.get_objects():
         try:
             if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):
-                logging.info(type(obj), obj.size())
+                log.info(type(obj), obj.size())
         except Exception:
             pass
 
diff --git a/pytorch_lightning/loggers/__init__.py b/pytorch_lightning/loggers/__init__.py
new file mode 100644
index 0000000000000..23c4b722229c0
--- /dev/null
+++ b/pytorch_lightning/loggers/__init__.py
@@ -0,0 +1,113 @@
+"""
+Lightning supports most popular logging frameworks (Tensorboard, comet, weights and biases, etc...).
+To use a logger, simply pass it into the trainer.
+
+.. code-block:: python
+
+    from pytorch_lightning import loggers
+
+    # lightning uses tensorboard by default
+    tb_logger = loggers.TensorBoardLogger()
+    trainer = Trainer(logger=tb_logger)
+
+    # or choose from any of the others such as MLFlow, Comet, Neptune, Wandb
+    comet_logger = loggers.CometLogger()
+    trainer = Trainer(logger=comet_logger)
+
+.. note:: All loggers log by default to `os.getcwd()`. To change the path without creating a logger set
+    Trainer(default_save_path='/your/path/to/save/checkpoints')
+
+Custom logger
+-------------
+
+You can implement your own logger by writing a class that inherits from
+`LightningLoggerBase`. Use the `rank_zero_only` decorator to make sure that
+only the first process in DDP training logs data.
+
+.. code-block:: python
+
+    from pytorch_lightning.loggers import LightningLoggerBase, rank_zero_only
+
+    class MyLogger(LightningLoggerBase):
+
+        @rank_zero_only
+        def log_hyperparams(self, params):
+            # params is an argparse.Namespace
+            # your code to record hyperparameters goes here
+            pass
+
+        @rank_zero_only
+        def log_metrics(self, metrics, step):
+            # metrics is a dictionary of metric names and values
+            # your code to record metrics goes here
+            pass
+
+        def save(self):
+            # Optional. Any code necessary to save logger data goes here
+            pass
+
+        @rank_zero_only
+        def finalize(self, status):
+            # Optional. Any code that needs to be run after training
+            # finishes goes here
+
+
+If you write a logger than may be useful to others, please send
+a pull request to add it to Lighting!
+
+Using loggers
+-------------
+
+Call the logger anywhere from your LightningModule by doing:
+
+.. code-block:: python
+
+    def train_step(...):
+        # example
+        self.logger.experiment.whatever_method_summary_writer_supports(...)
+
+    def any_lightning_module_function_or_hook(...):
+        self.logger.experiment.add_histogram(...)
+
+Supported Loggers
+-----------------
+"""
+from os import environ
+
+from .base import LightningLoggerBase, rank_zero_only
+from .tensorboard import TensorBoardLogger
+
+__all__ = ['TensorBoardLogger']
+
+try:
+    # needed to prevent ImportError and duplicated logs.
+    environ["COMET_DISABLE_AUTO_LOGGING"] = "1"
+
+    from .comet import CometLogger
+    __all__.append('CometLogger')
+except ImportError:
+    del environ["COMET_DISABLE_AUTO_LOGGING"]
+
+try:
+    from .mlflow import MLFlowLogger
+    __all__.append('MLFlowLogger')
+except ImportError:
+    pass
+
+try:
+    from .neptune import NeptuneLogger
+    __all__.append('NeptuneLogger')
+except ImportError:
+    pass
+
+try:
+    from .test_tube import TestTubeLogger
+    __all__.append('TestTubeLogger')
+except ImportError:
+    pass
+
+try:
+    from .wandb import WandbLogger
+    __all__.append('WandbLogger')
+except ImportError:
+    pass
diff --git a/pytorch_lightning/logging/base.py b/pytorch_lightning/loggers/base.py
similarity index 100%
rename from pytorch_lightning/logging/base.py
rename to pytorch_lightning/loggers/base.py
diff --git a/pytorch_lightning/logging/comet.py b/pytorch_lightning/loggers/comet.py
similarity index 97%
rename from pytorch_lightning/logging/comet.py
rename to pytorch_lightning/loggers/comet.py
index fbf4f839cda7a..1b5950c15226b 100644
--- a/pytorch_lightning/logging/comet.py
+++ b/pytorch_lightning/loggers/comet.py
@@ -31,7 +31,7 @@ def __init__(self, api_key=None, save_dir=None, workspace=None,
         .. code-block:: python
 
             # ONLINE MODE
-            from pytorch_lightning.logging import CometLogger
+            from pytorch_lightning.loggers import CometLogger
 
             # arguments made to CometLogger are passed on to the comet_ml.Experiment class
             comet_logger = CometLogger(
@@ -47,7 +47,7 @@ def __init__(self, api_key=None, save_dir=None, workspace=None,
         .. code-block:: python
 
             # OFFLINE MODE
-            from pytorch_lightning.logging import CometLogger
+            from pytorch_lightning.loggers import CometLogger
 
             # arguments made to CometLogger are passed on to the comet_ml.Experiment class
             comet_logger = CometLogger(
diff --git a/pytorch_lightning/logging/comet_logger.py b/pytorch_lightning/loggers/comet_logger.py
similarity index 81%
rename from pytorch_lightning/logging/comet_logger.py
rename to pytorch_lightning/loggers/comet_logger.py
index 4ea0c0714bec9..a5c5a78680031 100644
--- a/pytorch_lightning/logging/comet_logger.py
+++ b/pytorch_lightning/loggers/comet_logger.py
@@ -7,4 +7,4 @@
 warnings.warn("`comet_logger` module has been renamed to `comet` since v0.6.0"
               " and will be removed in v0.8.0", DeprecationWarning)
 
-from pytorch_lightning.logging.comet import CometLogger  # noqa: E402
+from pytorch_lightning.loggers.comet import CometLogger  # noqa: E402
diff --git a/pytorch_lightning/logging/mlflow.py b/pytorch_lightning/loggers/mlflow.py
similarity index 98%
rename from pytorch_lightning/logging/mlflow.py
rename to pytorch_lightning/loggers/mlflow.py
index 50f4843e0f6c9..652e32f675aae 100644
--- a/pytorch_lightning/logging/mlflow.py
+++ b/pytorch_lightning/loggers/mlflow.py
@@ -3,7 +3,7 @@
 
 .. code-block:: python
 
-    from pytorch_lightning.logging import MLFlowLogger
+    from pytorch_lightning.loggers import MLFlowLogger
     mlf_logger = MLFlowLogger(
         experiment_name="default",
         tracking_uri="file:/."
diff --git a/pytorch_lightning/logging/mlflow_logger.py b/pytorch_lightning/loggers/mlflow_logger.py
similarity index 82%
rename from pytorch_lightning/logging/mlflow_logger.py
rename to pytorch_lightning/loggers/mlflow_logger.py
index aa56f5ff7f68d..30993374ee9a1 100644
--- a/pytorch_lightning/logging/mlflow_logger.py
+++ b/pytorch_lightning/loggers/mlflow_logger.py
@@ -7,4 +7,4 @@
 warnings.warn("`mlflow_logger` module has been renamed to `mlflow` since v0.6.0"
               " and will be removed in v0.8.0", DeprecationWarning)
 
-from pytorch_lightning.logging.mlflow import MLFlowLogger  # noqa: E402
+from pytorch_lightning.loggers.mlflow import MLFlowLogger  # noqa: E402
diff --git a/pytorch_lightning/logging/neptune.py b/pytorch_lightning/loggers/neptune.py
similarity index 97%
rename from pytorch_lightning/logging/neptune.py
rename to pytorch_lightning/loggers/neptune.py
index 7c677962df70e..066ad9156d3b2 100644
--- a/pytorch_lightning/logging/neptune.py
+++ b/pytorch_lightning/loggers/neptune.py
@@ -6,7 +6,7 @@
 
 .. code-block:: python
 
-    from pytorch_lightning.logging import NeptuneLogger
+    from pytorch_lightning.loggers import NeptuneLogger
     # arguments made to NeptuneLogger are passed on to the neptune.experiments.Experiment class
 
     neptune_logger = NeptuneLogger(
@@ -48,7 +48,7 @@ def any_lightning_module_function_or_hook(...):
 from torch import is_tensor
 
 # from .base import LightningLoggerBase, rank_zero_only
-from pytorch_lightning.logging.base import LightningLoggerBase, rank_zero_only
+from pytorch_lightning.loggers.base import LightningLoggerBase, rank_zero_only
 
 logger = getLogger(__name__)
 
@@ -66,7 +66,7 @@ def __init__(self, api_key=None, project_name=None, offline_mode=False,
         .. code-block:: python
 
             # ONLINE MODE
-            from pytorch_lightning.logging import NeptuneLogger
+            from pytorch_lightning.loggers import NeptuneLogger
             # arguments made to NeptuneLogger are passed on to the neptune.experiments.Experiment class
 
             neptune_logger = NeptuneLogger(
@@ -81,7 +81,7 @@ def __init__(self, api_key=None, project_name=None, offline_mode=False,
         .. code-block:: python
 
             # OFFLINE MODE
-            from pytorch_lightning.logging import NeptuneLogger
+            from pytorch_lightning.loggers import NeptuneLogger
             # arguments made to NeptuneLogger are passed on to the neptune.experiments.Experiment class
 
             neptune_logger = NeptuneLogger(
diff --git a/pytorch_lightning/logging/tensorboard.py b/pytorch_lightning/loggers/tensorboard.py
similarity index 100%
rename from pytorch_lightning/logging/tensorboard.py
rename to pytorch_lightning/loggers/tensorboard.py
diff --git a/pytorch_lightning/logging/test_tube.py b/pytorch_lightning/loggers/test_tube.py
similarity index 98%
rename from pytorch_lightning/logging/test_tube.py
rename to pytorch_lightning/loggers/test_tube.py
index 10e14a502a37b..30509c7d3cf63 100644
--- a/pytorch_lightning/logging/test_tube.py
+++ b/pytorch_lightning/loggers/test_tube.py
@@ -6,7 +6,7 @@
 
 .. code-block:: python
 
-    from pytorch_lightning.logging import TestTubeLogger
+    from pytorch_lightning.loggers import TestTubeLogger
     tt_logger = TestTubeLogger(
         save_dir=".",
         name="default",
diff --git a/pytorch_lightning/logging/test_tube_logger.py b/pytorch_lightning/loggers/test_tube_logger.py
similarity index 82%
rename from pytorch_lightning/logging/test_tube_logger.py
rename to pytorch_lightning/loggers/test_tube_logger.py
index b76d526de92c1..bc531c0560643 100644
--- a/pytorch_lightning/logging/test_tube_logger.py
+++ b/pytorch_lightning/loggers/test_tube_logger.py
@@ -7,4 +7,4 @@
 warnings.warn("`test_tube_logger` module has been renamed to `test_tube` since v0.6.0"
               " and will be removed in v0.8.0", DeprecationWarning)
 
-from pytorch_lightning.logging.test_tube import TestTubeLogger  # noqa: E402
+from pytorch_lightning.loggers.test_tube import TestTubeLogger  # noqa: E402
diff --git a/pytorch_lightning/logging/wandb.py b/pytorch_lightning/loggers/wandb.py
similarity index 98%
rename from pytorch_lightning/logging/wandb.py
rename to pytorch_lightning/loggers/wandb.py
index 6e4307492623a..123b7544c9768 100644
--- a/pytorch_lightning/logging/wandb.py
+++ b/pytorch_lightning/loggers/wandb.py
@@ -25,7 +25,7 @@ class WandbLogger(LightningLoggerBase):
     --------
     .. code-block:: python
 
-        from pytorch_lightning.logging import WandbLogger
+        from pytorch_lightning.loggers import WandbLogger
         from pytorch_lightning import Trainer
 
         wandb_logger = WandbLogger()
diff --git a/pytorch_lightning/logging/__init__.py b/pytorch_lightning/logging/__init__.py
index 4850494f338bc..93515eb1eff31 100644
--- a/pytorch_lightning/logging/__init__.py
+++ b/pytorch_lightning/logging/__init__.py
@@ -1,115 +1,13 @@
 """
-Lightning supports most popular logging frameworks (Tensorboard, comet, weights and biases, etc...).
-To use a logger, simply pass it into the trainer.
-
-.. code-block:: python
-
-    from pytorch_lightning import logging
-
-    # lightning uses tensorboard by default
-    tb_logger = logging.TensorBoardLogger()
-    trainer = Trainer(logger=tb_logger)
-
-    # or choose from any of the others such as MLFlow, Comet, Neptune, Wandb
-    comet_logger = logging.CometLogger()
-    trainer = Trainer(logger=comet_logger)
-
-.. note:: All loggers log by default to `os.getcwd()`. To change the path without creating a logger set
-    Trainer(default_save_path='/your/path/to/save/checkpoints')
-
-Custom logger
--------------
-
-You can implement your own logger by writing a class that inherits from
-`LightningLoggerBase`. Use the `rank_zero_only` decorator to make sure that
-only the first process in DDP training logs data.
-
-.. code-block:: python
-
-    from pytorch_lightning.logging import LightningLoggerBase, rank_zero_only
-
-    class MyLogger(LightningLoggerBase):
-
-        @rank_zero_only
-        def log_hyperparams(self, params):
-            # params is an argparse.Namespace
-            # your code to record hyperparameters goes here
-            pass
-
-        @rank_zero_only
-        def log_metrics(self, metrics, step):
-            # metrics is a dictionary of metric names and values
-            # your code to record metrics goes here
-            pass
-
-        def save(self):
-            # Optional. Any code necessary to save logger data goes here
-            pass
-
-        @rank_zero_only
-        def finalize(self, status):
-            # Optional. Any code that needs to be run after training
-            # finishes goes here
-
-
-If you write a logger than may be useful to others, please send
-a pull request to add it to Lighting!
-
-Using loggers
--------------
-
-Call the logger anywhere from your LightningModule by doing:
-
-.. code-block:: python
-
-    def train_step(...):
-        # example
-        self.logger.experiment.whatever_method_summary_writer_supports(...)
-
-    def any_lightning_module_function_or_hook(...):
-        self.logger.experiment.add_histogram(...)
-
-Supported Loggers
------------------
+.. warning:: `logging` package has been renamed to `loggers` since v0.6.1 and will be removed in v0.8.0
 """
-from os import environ
-
-from .base import LightningLoggerBase, rank_zero_only
-from .tensorboard import TensorBoardLogger
-
-loggers = ['TensorBoardLogger']
-
-try:
-    # needed to prevent ImportError and duplicated logs.
-    environ["COMET_DISABLE_AUTO_LOGGING"] = "1"
-
-    from .comet import CometLogger
-    loggers.append('CometLogger')
-except ImportError:
-    del environ["COMET_DISABLE_AUTO_LOGGING"]
-
-try:
-    from .mlflow import MLFlowLogger
-    loggers.append('MLFlowLogger')
-except ImportError:
-    pass
-
-try:
-    from .neptune import NeptuneLogger
-    loggers.append('NeptuneLogger')
-except ImportError:
-    pass
 
-try:
-    from .test_tube import TestTubeLogger
-    loggers.append('TestTubeLogger')
-except ImportError:
-    pass
+import warnings
 
-try:
-    from .wandb import WandbLogger
-    loggers.append('WandbLogger')
-except ImportError:
-    pass
+warnings.warn("`logging` package has been renamed to `loggers` since v0.6.1"
+              " and will be removed in v0.8.0", DeprecationWarning)
 
-__all__ = loggers
+from pytorch_lightning.loggers import *  # noqa: F403
+from pytorch_lightning.loggers import (  # noqa: E402
+    base, comet, mlflow, neptune, tensorboard, test_tube, wandb
+)
diff --git a/pytorch_lightning/trainer/auto_mix_precision.py b/pytorch_lightning/trainer/auto_mix_precision.py
index 2915f2465fbb9..bd6edd9310116 100644
--- a/pytorch_lightning/trainer/auto_mix_precision.py
+++ b/pytorch_lightning/trainer/auto_mix_precision.py
@@ -7,7 +7,7 @@
     APEX_AVAILABLE = True
 except ImportError:
     APEX_AVAILABLE = False
-import logging
+import logging as log
 
 
 class TrainerAMPMixin(ABC):
@@ -15,7 +15,7 @@ class TrainerAMPMixin(ABC):
     def init_amp(self, use_amp):
         self.use_amp = use_amp and APEX_AVAILABLE
         if self.use_amp:
-            logging.info('using 16bit precision')
+            log.info('Using 16bit precision.')
 
         if use_amp and not APEX_AVAILABLE:  # pragma: no cover
             msg = """
diff --git a/pytorch_lightning/trainer/distrib_data_parallel.py b/pytorch_lightning/trainer/distrib_data_parallel.py
index 0561a6d40e3ff..b66981b995450 100644
--- a/pytorch_lightning/trainer/distrib_data_parallel.py
+++ b/pytorch_lightning/trainer/distrib_data_parallel.py
@@ -113,7 +113,7 @@ def train_fx(trial_hparams, cluster_manager, _):
 
 """
 
-import logging
+import logging as log
 import os
 import re
 import warnings
@@ -205,7 +205,7 @@ def set_distributed_mode(self, distributed_backend, num_gpu_nodes):
                 'or distributed_backend=ddp2'
             raise MisconfigurationException(w)
 
-        logging.info(f'gpu available: {torch.cuda.is_available()}, used: {self.on_gpu}')
+        log.info(f'GPU available: {torch.cuda.is_available()}, used: {self.on_gpu}')
 
     def configure_slurm_ddp(self, num_gpu_nodes):
         self.is_slurm_managing_tasks = False
@@ -253,7 +253,7 @@ def set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids):
                 gpu_str = ','.join([str(x) for x in data_parallel_device_ids])
                 os.environ["CUDA_VISIBLE_DEVICES"] = gpu_str
 
-        logging.info(f'VISIBLE GPUS: {os.environ["CUDA_VISIBLE_DEVICES"]}')
+        log.info(f'VISIBLE GPUS: {os.environ["CUDA_VISIBLE_DEVICES"]}')
 
     def ddp_train(self, gpu_idx, model):
         """
diff --git a/pytorch_lightning/trainer/logging.py b/pytorch_lightning/trainer/logging.py
index adc6ec5d9652c..9ff5c3db85076 100644
--- a/pytorch_lightning/trainer/logging.py
+++ b/pytorch_lightning/trainer/logging.py
@@ -3,7 +3,7 @@
 import torch
 
 from pytorch_lightning.core import memory
-from pytorch_lightning.logging import TensorBoardLogger
+from pytorch_lightning.loggers import TensorBoardLogger
 
 
 class TrainerLoggingMixin(ABC):
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
index e1c1464c73cf8..5794ba617c99f 100644
--- a/pytorch_lightning/trainer/trainer.py
+++ b/pytorch_lightning/trainer/trainer.py
@@ -1,8 +1,7 @@
 import os
 import sys
 import warnings
-import logging
-
+import logging as log
 
 import torch
 import torch.distributed as dist
@@ -97,7 +96,7 @@ def __init__(
             logger (:class:`.Logger`): Logger for experiment tracking.
                 Example::
 
-                    from pytorch_lightning.logging import TensorBoardLogger
+                    from pytorch_lightning.loggers import TensorBoardLogger
 
                     # default logger used by trainer
                     logger = TensorBoardLogger(
@@ -526,7 +525,7 @@ def __init__(
             Running in fast_dev_run mode: will run a full train,
             val loop using a single batch
             '''
-            logging.info(m)
+            log.info(m)
 
         # set default save path if user didn't provide one
         self.default_save_path = default_save_path
diff --git a/pytorch_lightning/trainer/training_io.py b/pytorch_lightning/trainer/training_io.py
index 3c489132c739c..2ee77ada4c5c4 100644
--- a/pytorch_lightning/trainer/training_io.py
+++ b/pytorch_lightning/trainer/training_io.py
@@ -39,7 +39,7 @@
 .. code-block:: python
 
     from pytorch_lightning import Trainer
-    from pytorch_lightning.logging import TestTubeLogger
+    from pytorch_lightning.loggers import TestTubeLogger
 
     logger = TestTubeLogger(
         save_dir='./savepath',
@@ -89,7 +89,7 @@
 
 """
 
-import logging
+import logging as log
 import os
 import re
 import signal
@@ -203,7 +203,7 @@ def restore_state_if_checkpoint_exists(self, model):
         if last_ckpt_name is not None:
             last_ckpt_path = os.path.join(self.checkpoint_callback.filepath, last_ckpt_name)
             self.restore(last_ckpt_path, self.on_gpu)
-            logging.info(f'model and trainer restored from checkpoint: {last_ckpt_path}')
+            log.info(f'Model and Trainer restored from checkpoint: {last_ckpt_path}')
             did_restore = True
 
         return did_restore
@@ -222,14 +222,14 @@ def register_slurm_signal_handlers(self):
             pass
 
         if on_slurm:
-            logging.info('set slurm handle signals')
+            log.info('Set SLURM handle signals.')
             signal.signal(signal.SIGUSR1, self.sig_handler)
             signal.signal(signal.SIGTERM, self.term_handler)
 
     def sig_handler(self, signum, frame):
         if self.proc_rank == 0:
             # save weights
-            logging.info('handling SIGUSR1')
+            log.info('handling SIGUSR1')
             self.hpc_save(self.weights_save_path, self.logger)
 
             # find job id
@@ -237,21 +237,21 @@ def sig_handler(self, signum, frame):
             cmd = 'scontrol requeue {}'.format(job_id)
 
             # requeue job
-            logging.info('\nrequeing job {job_id}...')
+            log.info(f'requeing job {job_id}...')
             result = call(cmd, shell=True)
 
             # print result text
             if result == 0:
-                logging.info('requeued exp {job_id}')
+                log.info(f'requeued exp {job_id}')
             else:
-                logging.info('requeue failed...')
+                log.info('requeue failed...')
 
             # close experiment to avoid issues
             self.logger.close()
 
     def term_handler(self, signum, frame):
         # save
-        logging.info("bypassing sigterm")
+        log.info("bypassing sigterm")
 
     # --------------------
     # MODEL SAVE CHECKPOINT
@@ -461,7 +461,7 @@ def hpc_load(self, folderpath, on_gpu):
         # call model hook
         model.on_hpc_load(checkpoint)
 
-        logging.info(f'restored hpc model from: {filepath}')
+        log.info(f'restored hpc model from: {filepath}')
 
     def max_ckpt_in_folder(self, path, name_key='ckpt_'):
         files = os.listdir(path)
diff --git a/pytorch_lightning/trainer/training_tricks.py b/pytorch_lightning/trainer/training_tricks.py
index 227ef245de47e..7fa4059afc3e2 100644
--- a/pytorch_lightning/trainer/training_tricks.py
+++ b/pytorch_lightning/trainer/training_tricks.py
@@ -1,4 +1,4 @@
-import logging
+import logging as log
 from abc import ABC, abstractmethod
 
 import torch
@@ -27,7 +27,7 @@ def print_nan_gradients(self):
         model = self.get_model()
         for param in model.parameters():
             if (param.grad is not None) and torch.isnan(param.grad.float()).any():
-                logging.info(param, param.grad)
+                log.info(param, param.grad)
 
     def configure_accumulated_gradients(self, accumulate_grad_batches):
         self.accumulate_grad_batches = None
diff --git a/tests/test_logging.py b/tests/test_logging.py
index a91f6087d9cb2..1b531420c8541 100644
--- a/tests/test_logging.py
+++ b/tests/test_logging.py
@@ -6,10 +6,14 @@
 
 import tests.utils as tutils
 from pytorch_lightning import Trainer
-from pytorch_lightning.logging import (
+from pytorch_lightning.loggers import (
     LightningLoggerBase,
     rank_zero_only,
     TensorBoardLogger,
+    MLFlowLogger,
+    CometLogger,
+    WandbLogger,
+    NeptuneLogger
 )
 from pytorch_lightning.testing import LightningTestModel
 
@@ -63,11 +67,6 @@ def test_mlflow_logger(tmpdir):
     """Verify that basic functionality of mlflow logger works."""
     tutils.reset_seed()
 
-    try:
-        from pytorch_lightning.logging import MLFlowLogger
-    except ModuleNotFoundError:
-        return
-
     hparams = tutils.get_hparams()
     model = LightningTestModel(hparams)
 
@@ -91,11 +90,6 @@ def test_mlflow_pickle(tmpdir):
     """Verify that pickling trainer with mlflow logger works."""
     tutils.reset_seed()
 
-    try:
-        from pytorch_lightning.logging import MLFlowLogger
-    except ModuleNotFoundError:
-        return
-
     # hparams = tutils.get_hparams()
     # model = LightningTestModel(hparams)
 
@@ -123,11 +117,6 @@ def test_comet_logger(tmpdir, monkeypatch):
 
     tutils.reset_seed()
 
-    try:
-        from pytorch_lightning.logging import CometLogger
-    except ModuleNotFoundError:
-        return
-
     hparams = tutils.get_hparams()
     model = LightningTestModel(hparams)
 
@@ -164,11 +153,6 @@ def test_comet_pickle(tmpdir, monkeypatch):
 
     tutils.reset_seed()
 
-    try:
-        from pytorch_lightning.logging import CometLogger
-    except ModuleNotFoundError:
-        return
-
     # hparams = tutils.get_hparams()
     # model = LightningTestModel(hparams)
 
@@ -197,18 +181,14 @@ def test_wandb_logger(tmpdir):
     """Verify that basic functionality of wandb logger works."""
     tutils.reset_seed()
 
-    from pytorch_lightning.logging import WandbLogger
-
     wandb_dir = os.path.join(tmpdir, "wandb")
-    logger = WandbLogger(save_dir=wandb_dir, anonymous=True)
+    _ = WandbLogger(save_dir=wandb_dir, anonymous=True)
 
 
 def test_neptune_logger(tmpdir):
     """Verify that basic functionality of neptune logger works."""
     tutils.reset_seed()
 
-    from pytorch_lightning.logging import NeptuneLogger
-
     hparams = tutils.get_hparams()
     model = LightningTestModel(hparams)
     logger = NeptuneLogger(offline_mode=True)
@@ -230,7 +210,6 @@ def test_wandb_pickle(tmpdir):
     """Verify that pickling trainer with wandb logger works."""
     tutils.reset_seed()
 
-    from pytorch_lightning.logging import WandbLogger
     wandb_dir = str(tmpdir)
     logger = WandbLogger(save_dir=wandb_dir, anonymous=True)
     assert logger is not None
@@ -240,8 +219,6 @@ def test_neptune_pickle(tmpdir):
     """Verify that pickling trainer with neptune logger works."""
     tutils.reset_seed()
 
-    from pytorch_lightning.logging import NeptuneLogger
-
     # hparams = tutils.get_hparams()
     # model = LightningTestModel(hparams)
 
diff --git a/tests/test_restore_models.py b/tests/test_restore_models.py
index 498b312913380..349e649e94592 100644
--- a/tests/test_restore_models.py
+++ b/tests/test_restore_models.py
@@ -1,4 +1,4 @@
-import logging
+import logging as log
 import os
 
 import torch
@@ -41,7 +41,7 @@ def test_running_test_pretrained_model_ddp(tmpdir):
     trainer = Trainer(**trainer_options)
     result = trainer.fit(model)
 
-    logging.info(os.listdir(tutils.get_data_path(logger, path_dir=tmpdir)))
+    log.info(os.listdir(tutils.get_data_path(logger, path_dir=tmpdir)))
 
     # correct result and ok accuracy
     assert result == 1, 'training failed to complete'
diff --git a/tests/utils.py b/tests/utils.py
index 70ecccb28d7a0..e3f84567dac2c 100644
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -8,7 +8,7 @@
 from pl_examples import LightningTemplateModel
 from pytorch_lightning import Trainer
 from pytorch_lightning.callbacks import ModelCheckpoint
-from pytorch_lightning.logging import TestTubeLogger, TensorBoardLogger
+from pytorch_lightning.loggers import TestTubeLogger, TensorBoardLogger
 from pytorch_lightning.testing import LightningTestModel
 
 # generate a list of random seeds for each test